When using real-world data, researchers must be careful to avoid several common pitfalls that can bias their results, according to Richard J. Willke, PhD, chief science officer of ISPOR.

Transcript (slightly modified)What are the challenges of working with real-world data? Do the benefits outweigh the negatives?
There are usually 3 challenges in working with real-world data. One is careful data collection. “Garbage in, garbage out” applies here. Another is the lack of randomization, of course, and that’s where we have a lot of different study methods and analytic methods that can be applied to correct for confounding and selection biases. Another one that we’ve seen is that there tend to be a lot of results generated from real-world data, and some people call that data-dredging. The danger is to pay more attention to results that you like than what you don’t.

But, the benefits are that you can find out, as I said a minute ago, things that you can’t find out with randomized clinical trial data, how it actually performs in patients. So, it has to be viewed, as I said, in a complementary sense. How does the real-world data track versus the randomized controlled trial data, and then you try to make decisions about how they fit together.